Predicting Accidents in Interlocking Systems: An SHA Model-Based Approach

doi:10.23940/ijpe.17.06.p11.897912

Abstract

Abstract: In recent days, rail transit accidents happen from time to time, but the causes are difficult to be found. According to the stochastic and real-time characteristics of equipment faults, three layer models based on stochastic hybrid automata (SHA) are proposed for interlocking systems. The three layer models consist of a system model, a monitoring model and a fault prediction model. The accidents caused by the equipment faults are predicted by simulating these models together on UPPAAL-SMC platform. The main contributions of this paper include: (1) extracting model patterns for interlocking systems (2) presenting a pattern-based system model generation process and an automatic generation method of monitoring model based on time constraints and (3) defining the accidents prediction model of collision accidents to predict the accidents and monitoring accident causes through model simulation.

Submitted on July 25, 2017; Revised on August 30, 2017; Accepted on September 15, 2017(This paper was presented at the Third International Symposium on System and Software Reliability.
References: 19

Yan Wang, Wen Zhong, Xiaohong Chen, and Jing Liu. Predicting Accidents in Interlocking Systems: An SHA Model-Based Approach [J]. Int J Performability Eng, 2017, 13(6): 897-912.

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